Subtle Land Subsidence Elevates Future Storm Surge Risks Along the Gulf Coast of the United States

Abstract We developed a robust InSAR processing strategy that can effectively mitigate severe decorrelation noise in a large volume of InSAR data. We mapped the average land subsidence rate (2017–2020) over the 131,572 km 2 Upper Texas and Louisiana coasts from Sentinel‐1 data, with ∼2 mm/yr accuracy based on independent GPS and tide gauge validation at 189 locations. The improved InSAR observations reveal widespread subsidence that was previously undetected in coastal wetlands and rural areas with small communities. Our InSAR surface deformation map is at the spatial scale that overlaps with the scale of hydrodynamic model grids. This allows us to integrate InSAR observations into operational storm surge models to analyze future flooding risks due to relative sea level change. We found that these subtle millimeter‐to‐centimeter subsidence features can substantially increase hurricane‐induced inundation, and passive flood mapping (known as the “bathtub” approach) can lead to inaccurate flood risk predictions.

Authors

Institutions

Publication Details

Journal
Journal of Geophysical Research Earth Surface
Published
2024-09-01
DOI
https://doi.org/10.1029/2024jf007858
Citations
12
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
Field-Weighted Citation Impact
15.59

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Subtle Land Subsidence Elevates Future Storm Surge Risks Along the Gulf Coast of the United States

Ke Wang, Srinivas Bettadpur, Eirik Valseth, Cathleen E. Jones et al.
12 citations
Journal of Geophysical Research Earth Surface
Synthetic Aperture Radar (SAR) Applications and Techniques
15.59
article

Subtle Land Subsidence Elevates Future Storm Surge Risks Along the Gulf Coast of the United States

Ke Wang, Srinivas Bettadpur, Eirik Valseth, Cathleen E. Jones, Clint N. Dawson, Jingyi Chen, Gordon Wells
article en
12 citations

Abstract

Abstract We developed a robust InSAR processing strategy that can effectively mitigate severe decorrelation noise in a large volume of InSAR data. We mapped the average land subsidence rate (2017–2020) over the 131,572 km 2 Upper Texas and Louisiana coasts from Sentinel‐1 data, with ∼2 mm/yr accuracy based on independent GPS and tide gauge validation at 189 locations. The improved InSAR observations reveal widespread subsidence that was previously undetected in coastal wetlands and rural areas with small communities. Our InSAR surface deformation map is at the spatial scale that overlaps with the scale of hydrodynamic model grids. This allows us to integrate InSAR observations into operational storm surge models to analyze future flooding risks due to relative sea level change. We found that these subtle millimeter‐to‐centimeter subsidence features can substantially increase hurricane‐induced inundation, and passive flood mapping (known as the “bathtub” approach) can lead to inaccurate flood risk predictions.

Journal of Geophysical Research Earth SurfaceVol. 129(9)
Jet Propulsion Laboratory (US), Simula Research Laboratory (NO), Norwegian University of Life Sciences (NO), The University of Texas at Austin (US)
National Science Foundation, National Aeronautics and Space Administration, Nuclear Safety and Security Commission, Jet Propulsion Laboratory
Life below water
Openalex Percentile: Top 1%
Synthetic Aperture Radar (SAR) Applications and Techniques
15.59
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.